Typical pit mines include both valuable ore (e.g. iron ore) as well as a large amount of waste material. In order to plan and execute mining operations for the extraction of the valuable ore, an accurate representation of the in-ground geology is advantageous.
In current practice it is known to acquire geophysical data such as natural gamma logs from the mine environment (e.g. from exploration drill holes). The natural gamma logs are then manually analysed (i.e. visually inspected) by a trained geologist in order to identify characteristic signatures of distinctive features (i.e. target features) in the underground geology. In the case of stratiform mine environments, such target features include marker bands of specific material (such as shale), the marker bands being distinct geological layers that help establish the geological sequence of the mine and any local warping (folding) and fracturing (faulting) that may be present. Given the relatively consistent geological structure of the mine, the identification of the positioning of marker bands allows geological boundaries between the marker bands and the target ore to be predicted.
The manual inspection of the geophysical data is labour intensive and errors or difficulties with reassessing results can arise.
It is also known to apply machine learning techniques, and in particular Gaussian processes, to automatically detect characteristic signatures of target features in geophysical data sets such as natural gamma logs. An example of this process is described Silversides, K L, Melkumyan, A, Wyman, D A, Hatherly, P J, Nettleton, E (2011) Detection of Geological Structure using Gamma Logs for Autonomous Mining. In IEEE International Conference on Robotics and Automation, 9-13 May, 2011. Shanghai, China, pp 1577-1582, the contents of which are hereby incorporated in their entirety into this specification by reference. While the use of such techniques improves on the efficiency of manual inspection techniques, the process of preparing training libraries and training the Gaussian process is a difficult and slow process and ultimately impacts on the accuracy of the predictions that can be made.
It would be desirable to provide systems and methods for processing geophysical data to identify target features that are more efficient and/or more accurate than existing systems and methods. Alternatively, or in addition, it would be desirable to provide a useful alternative to known systems and methods processing geophysical data to identify target features.
Reference to any prior art in the specification is not, and should not be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general knowledge in Australia or any other jurisdiction or that this prior art could reasonably be expected to be ascertained, understood and regarded as relevant by a person skilled in the art.